I found in literature that one of the most common way of standardization data is to compute z-scores (mean subtraction and division by standard deviation). Can anybody tell me if it is ok to compute ...

I currently read a paper in which the author has asked people 3 different questions regarding their life satisfaction, all of which are to be rated on a four point scale: 1) very low, 2) low, 3) high, ...

I found in literature that one of the most common way of standardization data is to compute z-scores (mean subtraction and division by standard deviation). Can anybody tell me if it is ok to compute ...

I'm solving this multiple choice question on the properties of a standardized variable. Two of the possible options (which are wrong but look right to me) are 1. It is always normally distributed and ...

I ran PCA in R using the principal() function in the "psych" package.
Suppose my dataset is called "data" (118 rows and 8 columns).
The 8 variables are answers from a questionnaire, with a scale that ...

Please note: This question pertains to Q Methodology, a research method used to study people's subjectivity. Q embodies ontological and epistemological assumptions that sometimes differ markedly from ...

How does standardization of data (subtracting the mean, dividing by standard deviation) affect classifiers? Namely, how (if at all) do different types of classifiers get affected by such an operation? ...

Im new to forum and have tried reading some articles to solve my problem but had trouble understanding some of it and others might not have been completely relevant. Below I will outline my problem ...

I standardized my explanatory variables so that each variable has a mean of 0 and standard deviation of 1 to improve convergence of the fitting algorithm and putting the estimated coefficients on the ...

It is generally agreed that in penalized regression models, such as ridge regression, the lasso, and the elastic net, one should standardize the predictors (such as dividing each by its SD) so that ...

To remove non stationarity in a time series, we can standardize the time series by subtracting the mean and dividing by the standard deviation. We can also keep differencing the time series until the ...

I have data on age, gender, height, weight and a health variable (call it yvar) which is a continuous variable in the range of 50-150. The age is in the range of 5-15 years (a study of children). I ...

I am dealing with data with different units of measurement for NYC neighborhoods and I am trying to build a composite score with it. For example, I have total population by neighborhood, mean income ...

Is it possible to retrieve the Unstandardised Regression Coefficients from a Standardised Regression? If so, how is does one do this in order to use the coefficients to make predictions on new data?
...

I have to perform an independent group t-test and have unequal variances. From what I understand, the solution is to transform the data. To transform it, do I standardize it? Or simple use log10 to ...

I have 2 groups/samples.
Correct me if I'm wrong, but before doing an independent-group t-test we have to verify the homogeneity of variance with Hartley's F-max test.
When doing this test, we have ...

In the context of a paper dealing with Statistical Quality Control, I am defining and using the concept of the mean shift divided by the standard deviation of a (normally distributed one-dimensional) ...

If I standardize my dependent and independent variable, and run a linear regression between them, the slope estimate which I have will be standardised. The variables were standardised by subtracting ...

If I have an autoregression with an exogenous variable and standardized the exogenous variable to better interpret the coefficients,
can I standardize the dependent autoregressive component also so ...

Is there a symbol to indicate that variables have been standardized?
For example, if I have 2 different scoring functions Score1 and Score2. Let's say I want to form a combo score and show that the ...

I'm currently doing two multiple linear regressions. Each of them with the same set of predictors (measurements for real estate quality) $X_1,...,X_n$, but with different dependent variables (one of ...

I have a variety of samples, each with a different standard deviation and mean. The coefficient of variation $CV$ = ${\sigma} / {\mu}$ defines the amount of variation in a population or sample around ...

I wonder if feature scaling like this makes always sense for neural networks:
Let $T$ be the training set and $x_i \in \mathbb{R}^n$ with $d_i \in T$ be the feature vector of $d_i$. Then add another ...